Computing an accurate radio navigation-based position solution in challenging signal environments such as urban canyons and areas of dense foliage can be difficult. In these challenging signal environments, fewer signals are available, and those signals that are available tend to yield less accurate measurements on a device due to environmental attenuation. One approach to improving the availability and quality of position solutions in challenging signal environments is to combine observations of radio navigation signals with input from other sensors or signals that measure some aspect of user or antenna motion between or during the measurement of radio navigation signals. The additional information improves the position solution by subtracting out antenna motion between epochs of radio navigation measurements, effectively allowing multiple epochs of measurements to be statistically combined to reduce error.
One approach to improving the availability and quality of position solutions blends measurements of radio navigation signals in a Kansan Filter with numerical integration of accelerometer and/or rate gyroscope measurements or the like to correct for antenna motion between epochs. For this approach, the numerical integration component is often called an inertial navigation system (BINS) or DR component. In this approach, the DR component is used to subtract antenna motion between epochs so multiple epochs of radio navigation measurements may be combined. However, because the DR component estimates motion from one epoch to the next, the DR component accumulates errors over time as that motion is combined over multiple epochs.
It is desirable to minimize accumulated motion errors by making the DR component more stable. This can be done by introducing motion constraints such as, for example, directions in which motion cannot occur or directions in which motion is limited. For pedestrians, for example, a step counting model may be used to limit distance traveled. A challenge of applying motion constraints is deciding when to apply the constraints. For example, a pedestrian step counting motion constraint may harm, rather than help a positioning solution if the step constraint is applied at times that do not correspond to steps. Applying the step constraint at times that correspond to steps is a challenging task, however, because motion varies widely from user to user. For example, some users step with their heels, while other users step with the pads of their feet. Further, that motion appears differently in sensors in a device held in a hand versus carried in, for example, a pocket or handbag. Thus timing the application of DR constraints is both difficult and critical to performance.